SOTAVerified

Active Learning

Active Learning is a paradigm in supervised machine learning which uses fewer training examples to achieve better optimization by iteratively training a predictor, and using the predictor in each iteration to choose the training examples which will increase its chances of finding better configurations and at the same time improving the accuracy of the prediction model

Source: Polystore++: Accelerated Polystore System for Heterogeneous Workloads

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 176–200 of 3073 papers

TitleStatusHype
DIAL: Deep Interactive and Active Learning for Semantic Segmentation in Remote SensingCode1
Rethinking the Data Annotation Process for Multi-view 3D Pose Estimation with Active Learning and Self-TrainingCode1
AcTune: Uncertainty-aware Active Self-Training for Semi-Supervised Active Learning with Pretrained Language ModelsCode1
Towards General and Efficient Active LearningCode1
Active learning with MaskAL reduces annotation effort for training Mask R-CNNCode1
Boosting Active Learning via Improving Test PerformanceCode1
Active Sensing for Communications by LearningCode1
A Survey: Deep Learning for Hyperspectral Image Classification with Few Labeled SamplesCode1
Active Learning for Domain Adaptation: An Energy-Based ApproachCode1
TALISMAN: Targeted Active Learning for Object Detection with Rare Classes and Slices using Submodular Mutual InformationCode1
DeepAL: Deep Active Learning in PythonCode1
Towards Fewer Annotations: Active Learning via Region Impurity and Prediction Uncertainty for Domain Adaptive Semantic SegmentationCode1
Active Learning at the ImageNet ScaleCode1
Active Learning Meets Optimized Item SelectionCode1
Fink: early supernovae Ia classification using active learningCode1
YMIR: A Rapid Data-centric Development Platform for Vision ApplicationsCode1
GFlowNet FoundationsCode1
Code-free development and deployment of deep segmentation models for digital pathologyCode1
Focusing on Potential Named Entities During Active Label AcquisitionCode1
Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational DataCode1
Conditioning Sparse Variational Gaussian Processes for Online Decision-makingCode1
Diversity Enhanced Active Learning with Strictly Proper Scoring RulesCode1
GeneDisco: A Benchmark for Experimental Design in Drug DiscoveryCode1
A Simple Baseline for Low-Budget Active LearningCode1
Active Learning for Improved Semi-Supervised Semantic Segmentation in Satellite ImagesCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2—Unverified
2PT4ALAccuracy93.1—Unverified
3Learning lossAccuracy91.01—Unverified
4CoreGCNAccuracy90.7—Unverified
5Core-setAccuracy89.92—Unverified
6Random Baseline (Resnet18)Accuracy88.45—Unverified
7Random Baseline (VGG16)Accuracy85.09—Unverified